1. Field of the Invention
This invention generally relates to detecting defects on a wafer using adaptive local thresholding and color filtering.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices such as ICs. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Inspection processes generally detect a significant amount of potential defects that are actually nuisance defects on the wafer or noise. Nuisance defects as that term is generally used in the art can be defined as defects that are detected on the wafer that the user does not care about or events that are detected as defects but are not any kind of actual defects. For instance, nuisance defects may be events that are detected as defects but are actually color variations in images of a wafer due to variations in a process performed on the wafer that do not have any effect on the devices being fabricated on the wafer. Therefore, detecting such nuisance defects and reporting them as actual defects to a user have a number of disadvantages such as obscuring actual defects that a user cares about and/or affect the device fabrication in a meaningful manner in the inspection results, obstructing process control attempts to correct the actual defects that a user cares about, and causing users to change the wafer inspection process in ways to reduce the detection of nuisance defects that cause fewer defects of interest to be detected.
Since nuisance defects will be detected by most every inspection process, many inspection processes have been developed in which defect detection is performed and. then the detected defects are filtered in some manner to separate defects of interest from nuisance defects. For instance, When there is color variation in images of a wafer that causes nuisance defects to be detected, defect size or energy attributes have been used to try to de-tune the color variation. Size and energy attributes can be calculated after pixels have been flagged as defect candidates. Both work to some extent for nuisance filtering but could not achieve the desired nuisance rate without dropping smaller, more important defects of interest. In addition, the size attribute can be unstable as it can be directly related to the threshold used for defect detection. In particular, for the same defect, if the threshold is higher, the size attribute could be smaller. In addition, energy attributes work to some extent for bigger, more spread out color variation, but substrates of different materials could have strongly concentrated differences. Those strongly concentrated differences usually have the same level of energy attribute reading as defects of interest. Therefore, when defect characteristics such as size and energy attributes are used for post-detection filtering, too many color nuisances may be included in the final inspection results.
Two-dimensional (2D) outlier detection is an efficient way of detecting defects on a wafer and can tolerate some levels of global color variation. However, when color variation is localized to images of some substrates but not others, 2D outlier detection cannot tell the difference between defects and color nuisances. 2D outlier detection can also lose the local spatial content of individual pixels in output generated for a wafer.
Accordingly, it would be advantageous to develop methods and systems for detecting defects on a wafer that can be used to more effectively reduce the nuisance rate of inspection processes for wafers with color variation in images of the wafers produced by the inspection processes.